Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors.

ABSTRACT: Machine learning-based chemical screening has made substantial progress in recent years. However, these predictions often have low accuracy and high uncertainty when identifying new active chemical scaffolds. Hence, a high proportion of retrieved compounds are not structurally novel. In this study, we proposed a strategy to address this issue by iteratively optimizing an evolutionary chemical binding similarity (ECBS) model using experimental validation data. Various data update and model retraining schemes were tested to efficiently incorporate new experimental data into ECBS models, resulting in a fine-tuned ECBS model with improved accuracy and coverage. To demonstrate the effectiveness of our approach, we identified the novel hit molecules for the mitogen-activated protein kinase kinas

SUBMITTER: Durai P 

PROVIDER: S-EPMC10517535 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature